{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 任务描述：\n",
    "\n",
    "### ✓代码跑通\n",
    "\n",
    "### 请在 MyCNN类中补全代码，构造卷积神经网络，保证程序跑通。\n",
    "\n",
    "### ✓调优\n",
    "\n",
    "### 思考并动手进行调优，以在验证集上的准确率为评价指标，验证集上准确率越高，得分越高！\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 任务描述：\n",
    "\n",
    "### 如何根据据图像的视觉内容为图像赋予一个语义类别是**图像分类**的目标，也是图像检索、图像内容分析和目标识别等问题的基础。\n",
    "\n",
    "### 本实践旨在通过一个美食分类的案列，让大家理解和掌握如何使用飞桨动态图搭建一个**卷积神经网络**。\n",
    "\n",
    "### 特别提示：本实践所用数据集均来自互联网，请勿用于商务用途。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import random\n",
    "import json\n",
    "import paddle\n",
    "import sys\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from PIL import ImageEnhance\n",
    "import paddle.fluid as fluid\n",
    "from multiprocessing import cpu_count\n",
    "import matplotlib.pyplot as plt\n",
    "from paddle.fluid.dygraph import Conv2D,BatchNorm,Linear,Pool2D,Dropout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'''\n",
    "参数配置\n",
    "'''\n",
    "train_parameters = {\n",
    "    \"input_size\": [3, 64, 64],                                #输入图片的shape\n",
    "    \"class_dim\": -1,                                          #分类数\n",
    "    \"src_path\":\"data/data42610/foods.zip\",                    #原始数据集路径\n",
    "    \"target_path\":\"/home/aistudio/data/\",                     #要解压的路径\n",
    "    \"train_list_path\": \"/home/aistudio/data/train.txt\",       #train.txt路径\n",
    "    \"eval_list_path\": \"/home/aistudio/data/eval.txt\",         #eval.txt路径\n",
    "    \"readme_path\": \"/home/aistudio/data/readme.json\",         #readme.json路径\n",
    "    \"label_dict\":{},                                          #标签字典\n",
    "    \"num_epochs\": 3,                                         #训练轮数\n",
    "    \"train_batch_size\": 128,                                   #训练时每个批次的大小\n",
    "    \"learning_strategy\": {                                    #优化函数相关的配置\n",
    "        \"lr\": 0.0001                                          #超参数学习率\n",
    "    } \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **一、数据准备**\n",
    "\n",
    "（1）解压原始数据集\n",
    "\n",
    "（2）按照比例划分训练集与验证集\n",
    "\n",
    "（3）乱序，生成数据列表\n",
    "\n",
    "（4）构造训练数据集提供器和验证数据集提供器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def unzip_data(src_path,target_path):\n",
    "    '''\n",
    "    解压原始数据集，将src_path路径下的zip包解压至target_path目录下\n",
    "    '''\n",
    "    if(not os.path.isdir(target_path + \"foods\")):     \n",
    "        z = zipfile.ZipFile(src_path, 'r')\n",
    "        z.extractall(path=target_path)\n",
    "        z.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_data_list(target_path,train_list_path,eval_list_path):\n",
    "    '''\n",
    "    生成数据列表\n",
    "    '''\n",
    "    #存放所有类别的信息\n",
    "    class_detail = []\n",
    "    #获取所有类别保存的文件夹名称\n",
    "    data_list_path=target_path+\"foods/\"\n",
    "    class_dirs = os.listdir(data_list_path)  \n",
    "    #总的图像数量\n",
    "    all_class_images = 0\n",
    "    #存放类别标签\n",
    "    class_label=0\n",
    "    #存放类别数目\n",
    "    class_dim = 0\n",
    "    #存储要写进eval.txt和train.txt中的内容\n",
    "    trainer_list=[]\n",
    "    eval_list=[]\n",
    "    #读取每个类别\n",
    "    for class_dir in class_dirs:\n",
    "        if class_dir != \".DS_Store\":\n",
    "            class_dim += 1\n",
    "            #每个类别的信息\n",
    "            class_detail_list = {}\n",
    "            eval_sum = 0\n",
    "            trainer_sum = 0\n",
    "            #统计每个类别有多少张图片\n",
    "            class_sum = 0\n",
    "            #获取类别路径 \n",
    "            path = data_list_path  + class_dir\n",
    "            # 获取所有图片\n",
    "            img_paths = os.listdir(path)\n",
    "            for img_path in img_paths:                                  # 遍历文件夹下的每个图片\n",
    "                name_path = path + '/' + img_path                       # 每张图片的路径\n",
    "                if class_sum % 8 == 0:                                  # 每8张图片取一个做验证数据\n",
    "                    eval_sum += 1                                       # test_sum为测试数据的数目\n",
    "                    eval_list.append(name_path + \"\\t%d\" % class_label + \"\\n\")\n",
    "                else:\n",
    "                    trainer_sum += 1 \n",
    "                    trainer_list.append(name_path + \"\\t%d\" % class_label + \"\\n\")#trainer_sum测试数据的数目\n",
    "                class_sum += 1                                          #每类图片的数目\n",
    "                all_class_images += 1                                   #所有类图片的数目\n",
    "             \n",
    "            # 说明的json文件的class_detail数据\n",
    "            class_detail_list['class_name'] = class_dir             #类别名称\n",
    "            class_detail_list['class_label'] = class_label          #类别标签\n",
    "            class_detail_list['class_eval_images'] = eval_sum       #该类数据的测试集数目\n",
    "            class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目\n",
    "            class_detail.append(class_detail_list)  \n",
    "            #初始化标签列表\n",
    "            train_parameters['label_dict'][str(class_label)] = class_dir\n",
    "            class_label += 1 \n",
    "            \n",
    "    #初始化分类数\n",
    "    train_parameters['class_dim'] = class_dim\n",
    "    \n",
    "    #乱序  \n",
    "    random.shuffle(eval_list)\n",
    "    with open(eval_list_path, 'a') as f:\n",
    "        for eval_image in eval_list:\n",
    "            f.write(eval_image) \n",
    "            \n",
    "    random.shuffle(trainer_list)\n",
    "    with open(train_list_path, 'a') as f2:\n",
    "        for train_image in trainer_list:\n",
    "            f2.write(train_image) \n",
    "\n",
    "    # 说明的json文件信息\n",
    "    readjson = {}\n",
    "    readjson['all_class_name'] = data_list_path                  #文件父目录\n",
    "    readjson['all_class_images'] = all_class_images\n",
    "    readjson['class_detail'] = class_detail\n",
    "    jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': '))\n",
    "    with open(train_parameters['readme_path'],'w') as f:\n",
    "        f.write(jsons)\n",
    "    print ('生成数据列表完成！')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def custom_reader(file_list):\n",
    "    '''\n",
    "    自定义reader\n",
    "    '''\n",
    "    def reader():\n",
    "        with open(file_list, 'r') as f:\n",
    "            lines = [line.strip() for line in f]\n",
    "            for line in lines:\n",
    "                img_path, lab = line.strip().split('\\t')\n",
    "                img = Image.open(img_path) \n",
    "                if img.mode != 'RGB': \n",
    "                    img = img.convert('RGB') \n",
    "                img = img.resize((64, 64), Image.BILINEAR)\n",
    "                img = np.array(img).astype('float32') \n",
    "                img = img.transpose((2, 0, 1))  # HWC to CHW \n",
    "                img = img/255                   # 像素值归一化 \n",
    "                yield img, int(lab) \n",
    "    return reader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成数据列表完成！\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "参数初始化\n",
    "'''\n",
    "src_path=train_parameters['src_path']\n",
    "target_path=train_parameters['target_path']\n",
    "train_list_path=train_parameters['train_list_path']\n",
    "eval_list_path=train_parameters['eval_list_path']\n",
    "batch_size=train_parameters['train_batch_size']\n",
    "\n",
    "'''\n",
    "解压原始数据到指定路径\n",
    "'''\n",
    "unzip_data(src_path,target_path)\n",
    "\n",
    "'''\n",
    "划分训练集与验证集，乱序，生成数据列表\n",
    "'''\n",
    "#每次生成数据列表前，首先清空train.txt和eval.txt\n",
    "with open(train_list_path, 'w') as f: \n",
    "    f.seek(0)\n",
    "    f.truncate() \n",
    "with open(eval_list_path, 'w') as f: \n",
    "    f.seek(0)\n",
    "    f.truncate() \n",
    "    \n",
    "#生成数据列表   \n",
    "get_data_list(target_path,train_list_path,eval_list_path)\n",
    "\n",
    "'''\n",
    "构造数据提供器\n",
    "'''\n",
    "train_reader = paddle.batch(custom_reader(train_list_path),\n",
    "                            batch_size=batch_size,\n",
    "                            drop_last=True)\n",
    "eval_reader = paddle.batch(custom_reader(eval_list_path),\n",
    "                            batch_size=batch_size,\n",
    "                            drop_last=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **二、模型配置**\n",
    "\n",
    "## ###在以下cell中完成卷积神经网络的定义###\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#定义网络\n",
    "class MyCNN(fluid.dygraph.Layer):\n",
    "    def __init__(self):\n",
    "        super(MyCNN,self).__init__()\n",
    "        self.CNN1=Conv2D(num_channels=3,stride=1,filter_size=5,padding=2,num_filters=32,act=\"relu\")\n",
    "        self.Pool1=Pool2D(pool_size=2,pool_stride=2,pool_type=\"max\")\n",
    "        self.norm1=BatchNorm(num_channels=32)\n",
    "        self.CNN2=Conv2D(num_channels=32,stride=1,filter_size=3,padding=1,num_filters=64,act=\"relu\")\n",
    "        self.Pool2=Pool2D(pool_size=2,pool_stride=2,pool_type=\"max\")\n",
    "        self.norm2=BatchNorm(num_channels=64)\n",
    "        self.CNN3=Conv2D(num_channels=64,stride=1,filter_size=3,padding=1,num_filters=64,act=\"relu\")\n",
    "        self.Pool3=Pool2D(pool_size=2,pool_stride=2,pool_type=\"max\")\n",
    "        self.norm3=BatchNorm(num_channels=64)\n",
    "        self.Fc1=Linear(input_dim=64*16*16,output_dim=1024,act=\"relu\")\n",
    "        self.Fc2=Linear(input_dim=1024,output_dim=5,act=\"softmax\")\n",
    "\n",
    "        self.Drop=Dropout(0.5)\n",
    "    def forward(self,input):        # forward 定义执行实际运行时网络的执行逻辑\n",
    "        '''前向计算'''\n",
    "        x=self.CNN1(input)\n",
    "        x=self.Drop(x)\n",
    "        x=self.Pool1(x)\n",
    "        #x=self.norm1(x)\n",
    "        x=self.CNN2(x)\n",
    "        x=self.Drop(x)\n",
    "        x=self.Pool2(x)\n",
    "        #x=self.norm2(x)\n",
    "        #x=self.CNN3(x)\n",
    "        #x=self.Drop(x)\n",
    "        #x=self.Pool3(x)\n",
    "        #x=self.norm3(x)\n",
    "        # print(x.shape)\n",
    "        x=fluid.layers.reshape(x,[x.shape[0],-1])\n",
    "        x=self.Fc1(x)\n",
    "        x=self.Fc2(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **三、模型训练 && 四、模型评估**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_train_iter=0\n",
    "all_train_iters=[]\n",
    "all_train_costs=[]\n",
    "all_train_accs=[]\n",
    "\n",
    "def draw_train_process(title,iters,costs,accs,label_cost,lable_acc):\n",
    "    plt.title(title, fontsize=24)\n",
    "    plt.xlabel(\"iter\", fontsize=20)\n",
    "    plt.ylabel(\"loss/acc\", fontsize=20)\n",
    "    plt.plot(iters, costs,color='red',label=label_cost) \n",
    "    plt.plot(iters, accs,color='green',label=lable_acc) \n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def draw_process(title,color,iters,data,label):\n",
    "    plt.title(title, fontsize=24)\n",
    "    plt.xlabel(\"iter\", fontsize=20)\n",
    "    plt.ylabel(label, fontsize=20)\n",
    "    plt.plot(iters, data,color=color,label=label) \n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n",
      "{}\n",
      "Loss at epoch 0 step 0: [1.9399188], acc: [0.0625]\n",
      "Loss at epoch 0 step 1: [3.9670951], acc: [0.25]\n",
      "Loss at epoch 0 step 2: [5.177949], acc: [0.25]\n",
      "Loss at epoch 0 step 3: [3.4276183], acc: [0.375]\n",
      "Loss at epoch 0 step 4: [4.47402], acc: [0.25]\n",
      "Loss at epoch 0 step 5: [2.1632786], acc: [0.25]\n",
      "Loss at epoch 0 step 6: [1.63076], acc: [0.25]\n",
      "Loss at epoch 0 step 7: [2.5510597], acc: [0.1875]\n",
      "Loss at epoch 0 step 8: [2.463718], acc: [0.25]\n",
      "Loss at epoch 0 step 9: [2.6786313], acc: [0.25]\n",
      "Loss at epoch 0 step 10: [2.5309076], acc: [0.3125]\n",
      "Loss at epoch 0 step 11: [2.3741262], acc: [0.125]\n",
      "Loss at epoch 0 step 12: [2.0067134], acc: [0.25]\n",
      "Loss at epoch 0 step 13: [1.8294327], acc: [0.25]\n",
      "Loss at epoch 0 step 14: [1.4621787], acc: [0.4375]\n",
      "Loss at epoch 0 step 15: [1.9662483], acc: [0.0625]\n",
      "Loss at epoch 0 step 16: [1.8950152], acc: [0.125]\n",
      "Loss at epoch 0 step 17: [1.8411678], acc: [0.1875]\n",
      "Loss at epoch 0 step 18: [1.9208685], acc: [0.1875]\n",
      "Loss at epoch 0 step 19: [1.4348668], acc: [0.4375]\n",
      "Loss at epoch 0 step 20: [1.5797461], acc: [0.3125]\n",
      "Loss at epoch 0 step 21: [1.6656027], acc: [0.375]\n",
      "Loss at epoch 0 step 22: [2.1107316], acc: [0.125]\n",
      "Loss at epoch 0 step 23: [1.5986416], acc: [0.375]\n",
      "Loss at epoch 0 step 24: [1.8695878], acc: [0.125]\n",
      "Loss at epoch 0 step 25: [1.6937158], acc: [0.25]\n",
      "Loss at epoch 0 step 26: [1.409558], acc: [0.5]\n",
      "Loss at epoch 0 step 27: [1.6694633], acc: [0.125]\n",
      "Loss at epoch 0 step 28: [1.499587], acc: [0.5]\n",
      "Loss at epoch 0 step 29: [1.558632], acc: [0.375]\n",
      "Loss at epoch 0 step 30: [2.252865], acc: [0.125]\n",
      "Loss at epoch 0 step 31: [1.7996583], acc: [0.1875]\n",
      "Loss at epoch 0 step 32: [1.5675936], acc: [0.1875]\n",
      "Loss at epoch 0 step 33: [1.4379275], acc: [0.4375]\n",
      "Loss at epoch 0 step 34: [1.6406398], acc: [0.25]\n",
      "Loss at epoch 0 step 35: [1.6460358], acc: [0.3125]\n",
      "Loss at epoch 0 step 36: [1.734961], acc: [0.1875]\n",
      "Loss at epoch 0 step 37: [1.6345848], acc: [0.125]\n",
      "Loss at epoch 0 step 38: [1.8190207], acc: [0.125]\n",
      "Loss at epoch 0 step 39: [1.5917151], acc: [0.375]\n",
      "Loss at epoch 0 step 40: [1.4644365], acc: [0.3125]\n",
      "Loss at epoch 0 step 41: [1.5626838], acc: [0.4375]\n",
      "Loss at epoch 0 step 42: [1.8733578], acc: [0.25]\n",
      "Loss at epoch 0 step 43: [1.6525459], acc: [0.25]\n",
      "Loss at epoch 0 step 44: [1.6619017], acc: [0.375]\n",
      "Loss at epoch 0 step 45: [1.6276131], acc: [0.375]\n",
      "Loss at epoch 0 step 46: [1.5287622], acc: [0.5]\n",
      "Loss at epoch 0 step 47: [1.4609015], acc: [0.375]\n",
      "Loss at epoch 0 step 48: [1.561856], acc: [0.375]\n",
      "Loss at epoch 0 step 49: [1.3701351], acc: [0.375]\n",
      "Loss at epoch 0 step 50: [1.4193866], acc: [0.3125]\n",
      "Loss at epoch 0 step 51: [1.4095552], acc: [0.375]\n",
      "Loss at epoch 0 step 52: [1.740967], acc: [0.25]\n",
      "Loss at epoch 0 step 53: [1.6913124], acc: [0.25]\n",
      "Loss at epoch 0 step 54: [1.4838591], acc: [0.3125]\n",
      "Loss at epoch 0 step 55: [1.473714], acc: [0.1875]\n",
      "Loss at epoch 0 step 56: [1.400823], acc: [0.375]\n",
      "Loss at epoch 0 step 57: [1.4854059], acc: [0.25]\n",
      "Loss at epoch 0 step 58: [1.7358088], acc: [0.0625]\n",
      "Loss at epoch 0 step 59: [1.6292334], acc: [0.1875]\n",
      "Loss at epoch 0 step 60: [1.5102303], acc: [0.25]\n",
      "Loss at epoch 0 step 61: [1.7277856], acc: [0.0625]\n",
      "Loss at epoch 0 step 62: [1.5777292], acc: [0.25]\n",
      "Loss at epoch 0 step 63: [1.3987923], acc: [0.375]\n",
      "Loss at epoch 0 step 64: [1.5374107], acc: [0.1875]\n",
      "Loss at epoch 0 step 65: [1.4547238], acc: [0.3125]\n",
      "Loss at epoch 0 step 66: [1.332077], acc: [0.375]\n",
      "Loss at epoch 0 step 67: [1.3831742], acc: [0.4375]\n",
      "Loss at epoch 0 step 68: [1.5603383], acc: [0.25]\n",
      "Loss at epoch 0 step 69: [1.586959], acc: [0.3125]\n",
      "Loss at epoch 0 step 70: [1.3758864], acc: [0.5]\n",
      "Loss at epoch 0 step 71: [1.6559498], acc: [0.25]\n",
      "Loss at epoch 0 step 72: [1.3878169], acc: [0.375]\n",
      "Loss at epoch 0 step 73: [1.468842], acc: [0.375]\n",
      "Loss at epoch 0 step 74: [1.3081717], acc: [0.375]\n",
      "Loss at epoch 0 step 75: [1.4780338], acc: [0.3125]\n",
      "Loss at epoch 0 step 76: [1.7923108], acc: [0.125]\n",
      "Loss at epoch 0 step 77: [1.3047783], acc: [0.4375]\n",
      "Loss at epoch 0 step 78: [1.4100803], acc: [0.375]\n",
      "Loss at epoch 0 step 79: [1.4482816], acc: [0.3125]\n",
      "Loss at epoch 0 step 80: [1.4633515], acc: [0.5625]\n",
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      "Loss at epoch 2 step 0: [1.1082666], acc: [0.5]\n",
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      "Loss at epoch 2 step 2: [0.9664457], acc: [0.6875]\n",
      "Loss at epoch 2 step 3: [1.5429859], acc: [0.1875]\n",
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      "Loss at epoch 2 step 6: [0.87144434], acc: [0.6875]\n",
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      "Loss at epoch 2 step 124: [1.2469424], acc: [0.4375]\n",
      "Loss at epoch 2 step 125: [1.1420693], acc: [0.4375]\n",
      "Loss at epoch 2 step 126: [0.75887406], acc: [0.75]\n",
      "Loss at epoch 2 step 127: [1.2269948], acc: [0.5]\n",
      "Loss at epoch 2 step 128: [0.74714947], acc: [0.6875]\n",
      "Loss at epoch 2 step 129: [1.2948942], acc: [0.4375]\n",
      "Loss at epoch 2 step 130: [1.0818257], acc: [0.5]\n",
      "Loss at epoch 2 step 131: [1.401263], acc: [0.4375]\n",
      "Loss at epoch 2 step 132: [1.259296], acc: [0.5]\n",
      "Loss at epoch 2 step 133: [1.0168072], acc: [0.4375]\n",
      "Loss at epoch 2 step 134: [1.1841838], acc: [0.5625]\n",
      "Loss at epoch 2 step 135: [1.1473732], acc: [0.5625]\n",
      "Loss at epoch 2 step 136: [1.1878167], acc: [0.5]\n",
      "Loss at epoch 2 step 137: [1.2331456], acc: [0.375]\n",
      "Loss at epoch 2 step 138: [1.183725], acc: [0.5625]\n",
      "Loss at epoch 2 step 139: [1.2584888], acc: [0.375]\n",
      "Loss at epoch 2 step 140: [1.0910403], acc: [0.5]\n",
      "Loss at epoch 2 step 141: [1.1611067], acc: [0.3125]\n",
      "Loss at epoch 2 step 142: [0.9379231], acc: [0.6875]\n",
      "Loss at epoch 2 step 143: [1.4081321], acc: [0.5]\n",
      "Loss at epoch 2 step 144: [0.725162], acc: [0.8125]\n",
      "Loss at epoch 2 step 145: [0.9734869], acc: [0.5625]\n",
      "Loss at epoch 2 step 146: [1.146307], acc: [0.4375]\n",
      "Loss at epoch 2 step 147: [0.7915771], acc: [0.625]\n",
      "Loss at epoch 2 step 148: [1.3707154], acc: [0.375]\n",
      "Loss at epoch 2 step 149: [0.9190706], acc: [0.625]\n",
      "Loss at epoch 2 step 150: [1.1992053], acc: [0.5]\n",
      "Loss at epoch 2 step 151: [0.9179438], acc: [0.5625]\n",
      "Loss at epoch 2 step 152: [0.9423159], acc: [0.625]\n",
      "Loss at epoch 2 step 153: [0.924093], acc: [0.6875]\n",
      "Loss at epoch 2 step 154: [0.92730874], acc: [0.625]\n",
      "Loss at epoch 2 step 155: [0.91763943], acc: [0.5625]\n",
      "Loss at epoch 2 step 156: [1.028633], acc: [0.625]\n",
      "Loss at epoch 2 step 157: [1.204067], acc: [0.5625]\n",
      "Loss at epoch 2 step 158: [1.0108941], acc: [0.625]\n",
      "Loss at epoch 2 step 159: [0.96981996], acc: [0.625]\n",
      "Loss at epoch 2 step 160: [1.1629633], acc: [0.5]\n",
      "Loss at epoch 2 step 161: [1.2986743], acc: [0.375]\n",
      "Loss at epoch 2 step 162: [1.2213483], acc: [0.3125]\n",
      "Loss at epoch 2 step 163: [1.0849917], acc: [0.6875]\n",
      "Loss at epoch 2 step 164: [1.3503314], acc: [0.625]\n",
      "Loss at epoch 2 step 165: [1.0054727], acc: [0.75]\n",
      "Loss at epoch 2 step 166: [1.3785655], acc: [0.5]\n",
      "Loss at epoch 2 step 167: [1.1335063], acc: [0.5625]\n",
      "Loss at epoch 2 step 168: [1.2944164], acc: [0.4375]\n",
      "Loss at epoch 2 step 169: [0.899037], acc: [0.6875]\n",
      "Loss at epoch 2 step 170: [1.0261323], acc: [0.5625]\n",
      "Loss at epoch 2 step 171: [0.9420476], acc: [0.5625]\n",
      "Loss at epoch 2 step 172: [1.0116918], acc: [0.5]\n",
      "Loss at epoch 2 step 173: [1.0888181], acc: [0.5]\n",
      "Loss at epoch 2 step 174: [1.1408162], acc: [0.625]\n",
      "Loss at epoch 2 step 175: [0.8264537], acc: [0.625]\n",
      "Loss at epoch 2 step 176: [1.2100611], acc: [0.3125]\n",
      "Loss at epoch 2 step 177: [1.4960539], acc: [0.3125]\n",
      "Loss at epoch 2 step 178: [0.7876076], acc: [0.75]\n",
      "Loss at epoch 2 step 179: [1.3323164], acc: [0.3125]\n",
      "Loss at epoch 2 step 180: [0.96719563], acc: [0.4375]\n",
      "Loss at epoch 2 step 181: [0.96436733], acc: [0.625]\n",
      "Loss at epoch 2 step 182: [1.1838286], acc: [0.625]\n",
      "Loss at epoch 2 step 183: [1.1161095], acc: [0.5625]\n",
      "Loss at epoch 2 step 184: [0.9633477], acc: [0.5625]\n",
      "Loss at epoch 2 step 185: [1.176322], acc: [0.5625]\n",
      "Loss at epoch 2 step 186: [1.089194], acc: [0.4375]\n",
      "Loss at epoch 2 step 187: [1.4353046], acc: [0.3125]\n",
      "Loss at epoch 2 step 188: [1.3998706], acc: [0.5]\n",
      "Loss at epoch 2 step 189: [1.3125141], acc: [0.625]\n",
      "Loss at epoch 2 step 190: [1.1851321], acc: [0.5]\n",
      "Loss at epoch 2 step 191: [1.113107], acc: [0.625]\n",
      "Loss at epoch 2 step 192: [1.3628529], acc: [0.5]\n",
      "Loss at epoch 2 step 193: [1.1590458], acc: [0.625]\n",
      "Loss at epoch 2 step 194: [1.1328795], acc: [0.625]\n",
      "Loss at epoch 2 step 195: [1.3498168], acc: [0.375]\n",
      "Loss at epoch 2 step 196: [0.6141062], acc: [0.875]\n",
      "Loss at epoch 2 step 197: [1.7278666], acc: [0.4375]\n",
      "Loss at epoch 2 step 198: [0.968676], acc: [0.625]\n",
      "Loss at epoch 2 step 199: [1.0588129], acc: [0.4375]\n",
      "Loss at epoch 2 step 200: [1.2172215], acc: [0.6875]\n",
      "Loss at epoch 2 step 201: [1.1383636], acc: [0.625]\n",
      "Loss at epoch 2 step 202: [1.101171], acc: [0.625]\n",
      "Loss at epoch 2 step 203: [0.9164168], acc: [0.625]\n",
      "Loss at epoch 2 step 204: [1.1921878], acc: [0.625]\n",
      "Loss at epoch 2 step 205: [1.4459525], acc: [0.4375]\n",
      "Loss at epoch 2 step 206: [1.3529047], acc: [0.3125]\n",
      "Loss at epoch 2 step 207: [0.823347], acc: [0.75]\n",
      "Loss at epoch 2 step 208: [1.2085], acc: [0.5625]\n",
      "Loss at epoch 2 step 209: [0.7048982], acc: [0.75]\n",
      "Loss at epoch 2 step 210: [0.94897586], acc: [0.6875]\n",
      "Loss at epoch 2 step 211: [1.636555], acc: [0.25]\n",
      "Loss at epoch 2 step 212: [1.0141393], acc: [0.5625]\n",
      "Loss at epoch 2 step 213: [0.91135055], acc: [0.6875]\n",
      "Loss at epoch 2 step 214: [0.9840236], acc: [0.5625]\n",
      "Loss at epoch 2 step 215: [1.0969665], acc: [0.625]\n",
      "Loss at epoch 2 step 216: [0.8272525], acc: [0.75]\n",
      "Loss at epoch 2 step 217: [0.90680206], acc: [0.75]\n",
      "Loss at epoch 2 step 218: [1.2088338], acc: [0.625]\n",
      "Loss at epoch 2 step 219: [1.1940367], acc: [0.5625]\n",
      "Loss at epoch 2 step 220: [1.508859], acc: [0.375]\n",
      "Loss at epoch 2 step 221: [1.3901013], acc: [0.3125]\n",
      "Loss at epoch 2 step 222: [0.95349336], acc: [0.5625]\n",
      "Loss at epoch 2 step 223: [0.98764664], acc: [0.6875]\n",
      "Loss at epoch 2 step 224: [1.0238326], acc: [0.625]\n",
      "Loss at epoch 2 step 225: [0.792696], acc: [0.625]\n",
      "Loss at epoch 2 step 226: [0.9806013], acc: [0.6875]\n",
      "Loss at epoch 2 step 227: [1.037519], acc: [0.625]\n",
      "Loss at epoch 2 step 228: [1.182894], acc: [0.625]\n",
      "Loss at epoch 2 step 229: [1.2372228], acc: [0.5625]\n",
      "Loss at epoch 2 step 230: [1.2788624], acc: [0.5]\n",
      "Loss at epoch 2 step 231: [1.2608407], acc: [0.5]\n",
      "Loss at epoch 2 step 232: [1.2873125], acc: [0.5]\n",
      "Loss at epoch 2 step 233: [1.3863084], acc: [0.375]\n",
      "Loss at epoch 2 step 234: [0.7736103], acc: [0.6875]\n",
      "Loss at epoch 2 step 235: [0.7794442], acc: [0.75]\n",
      "Loss at epoch 2 step 236: [0.8788851], acc: [0.6875]\n",
      "Loss at epoch 2 step 237: [1.3069104], acc: [0.625]\n",
      "Loss at epoch 2 step 238: [0.88196677], acc: [0.6875]\n",
      "Loss at epoch 2 step 239: [1.0617999], acc: [0.5625]\n",
      "Loss at epoch 2 step 240: [0.82531625], acc: [0.75]\n",
      "Loss at epoch 2 step 241: [1.0316148], acc: [0.5]\n",
      "Loss at epoch 2 step 242: [1.2498329], acc: [0.4375]\n",
      "Loss at epoch 2 step 243: [1.1405993], acc: [0.625]\n",
      "Loss at epoch 2 step 244: [0.8867011], acc: [0.6875]\n",
      "Loss at epoch 2 step 245: [1.1567755], acc: [0.5]\n",
      "Loss at epoch 2 step 246: [0.9792522], acc: [0.5625]\n",
      "Loss at epoch 2 step 247: [0.824285], acc: [0.625]\n",
      "Loss at epoch 2 step 248: [1.1768544], acc: [0.4375]\n",
      "Loss at epoch 2 step 249: [0.92541724], acc: [0.5625]\n",
      "Loss at epoch 2 step 250: [1.5762206], acc: [0.4375]\n",
      "Loss at epoch 2 step 251: [1.5578434], acc: [0.4375]\n",
      "Loss at epoch 2 step 252: [1.3371915], acc: [0.4375]\n",
      "Loss at epoch 2 step 253: [1.2107491], acc: [0.5]\n",
      "Loss at epoch 2 step 254: [1.143059], acc: [0.5625]\n",
      "Loss at epoch 2 step 255: [1.1554801], acc: [0.4375]\n",
      "Loss at epoch 2 step 256: [0.98643374], acc: [0.6875]\n",
      "Loss at epoch 2 step 257: [0.85775733], acc: [0.75]\n",
      "Loss at epoch 2 step 258: [1.5239768], acc: [0.4375]\n",
      "Loss at epoch 2 step 259: [1.1843275], acc: [0.5625]\n",
      "Loss at epoch 2 step 260: [1.1723716], acc: [0.4375]\n",
      "Loss at epoch 2 step 261: [1.2415748], acc: [0.4375]\n",
      "Loss at epoch 2 step 262: [1.1677969], acc: [0.4375]\n",
      "Loss at epoch 2 step 263: [1.4912403], acc: [0.3125]\n",
      "Loss at epoch 2 step 264: [0.6996342], acc: [0.8125]\n",
      "Loss at epoch 2 step 265: [1.0533891], acc: [0.4375]\n",
      "Loss at epoch 2 step 266: [1.328329], acc: [0.625]\n",
      "Loss at epoch 2 step 267: [1.0547961], acc: [0.4375]\n",
      "Loss at epoch 2 step 268: [0.851746], acc: [0.75]\n",
      "Loss at epoch 2 step 269: [0.8456476], acc: [0.75]\n",
      "Loss at epoch 2 step 270: [0.7370913], acc: [0.8125]\n",
      "Loss at epoch 2 step 271: [1.2118199], acc: [0.375]\n",
      "Loss at epoch 2 step 272: [1.1186802], acc: [0.625]\n"
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OHVSrVs11HNoNpShKwjjyyCPZsGED27ZtS0l6Bw4ciKsALG3Ek99q1apx5JFHuk5blYWilDXSWKuvXLmy32zpZJOdnU3Hjh1Tll66SWd+tRtKUcoa69fD3r3plkIpY6iyUJSySP/+6ZZAKWOoslCUssiMGemWQCljZKyyMMbcY4xZaoxZYowZZ4wpP6NYiqIoGUZGKgtjTDPgTqCziLQBKgJD0iuVoihK+SUjlYVDJaC6MaYSUAPYlGZ5FEVRyi0mUyfPGGPuAv4B7Ae+FpErg/gZCgwFaNy4cafx48e7SisvL49atWrFIW3pojzltzzlteqWLZx6+eXF59nTp6dRmtRQnp4vJCe/vXr1mi8inSP5y0hlYYypD0wEBgM5wIfARyLybqgwnTt3lnnz5rlKLzs7m549e7oKWxopT/ktT3ll7Vpo3tx7noHfdqIpV8+X5OTXGBOVssjUbqizgdUisk1E8oGPgdPSLJOiKEq5JVOVxTqgmzGmhjHGAL2B5WmWSVEUpdySkcpCRGYDHwE/AYuxco5Oq1CKoijlmIxdG0pEHgceT7cciqIoSoa2LBRFUZTMQpWFoiiKEhFVFoqiKEpEVFkoiqIoEVFloSiKokRElYWiKIoSEVUWiqIoSkRUWSiKoigRUWWhKIqiRESVhaIoihIRVRaKoihKRFRZKIqiKBFRZaEoiqJERJWFoiiKEhFVFoqiKEpEVFkoiqIoEVFloSiKokRElYWiKIoSEVUWiqIoSkRUWSiKoigRUWWhKIqiRESVhaIoihKRmJWFMeY4Y8w1xpiGIa43cq4fG794iqIoSibgpmUxDHgO2BPi+m7gWeABt0IpiqIomYUbZdETmCYi+cEuOu7/Bc6KQy5FURQlg3CjLJoBayL4WQcc4SJuRVEUJQNxoywOAXUi+KkNiIu4FUVRlAzEjbJYAvQ3xlQOdtEYUwU4H1gWj2CKoihK5uBGWbwLHA18YIxp4nvBOf8AOAoYG794iqIoSiZQyUWY0cAlwADgHGPMImAjdiyjHVADmAaMSpSQiqIoSnqJuWUhIkVAf2A4kA90AwY6x0PAP4H+jj9FURSlDOCmZeExj33EGPNX4ESgHpADrEiUkjDG1ANeB9pgB8tvEJEfExG3oiiKEhuulIUHRzEkayD7eWCqiAxyBs1rJCkdRVEUJQIZudyHMaYucAYwBkBEDolIjtv4FEVRlPgwIrFNhzDGvAZcBBwRbBa3Y1K7EZgoIre6EsqYDtiB9GVAe2A+cJeI7A3wNxQYCtC4ceNO48ePd5MceXl51KpVy1XY0kh5ym95ymvVLVs49fLLi8+zp09PozSpoTw9X0hOfnv16jVfRDpH9CgiMf2AlcC4CH7eA36JNW6f8J2BAuAU5/x54MlwYTp16iRumT59uuuwpZHylN/ylFdZs0YEvL9yQLl6vpKc/ALzJIpyOVOX+9gAbBCR2c75R8DJccSnKIqixEFGLvchIluA9caYlo5Tb3RGuKIoStrI5OU+7gDecyb9dcDO31AUJRQxjj8qSixk7HIfIrJARDqLSDsRuUhEdsUTn6IoiuIeXe5DURRFiYgu96EoiqJEJGOX+1AURVEyh0xe7iM1jBtH61degZkz0y2JosSHDnArSSQuZVEmuOIKDku3DIqiKBmOK2VhjDHAIOBc7MB21SDeRER6xyGboiiKkiHErCyMMVWBKUBPwGAn3xkfL+LjriiKopQB3MyzeAjoBTwFNMIqhizs8h5XAOuB8UCVxIioKIqipBs3yuJS4CcReVxEdnocRWSLiIwHzsLO4L47QTIqihINOsCtJBE3yuI44AefcwGKl/4Qkd+BL4Dr4pJMUWLhm29g+/Z0S6EoZRY3yiIfOOBzngslDIrWAq43P1KUmMjPh7PPhj590i2JosTPlCkwa1a6pSiBG2uoDVgLKA+/AqcG+OkI7ERRUkGRMw906dL0yqEoiaB/f3vMsG5FNy2LH4DTfM4/BdoaY143xvQ3xvwLOBvIToB8iqIoSgbgpmXxPnCUMaa5iKwBRmAXFbwBuB5rHfUbMCxRQiqKEgUZVhNVyhYxKwsRycan1SAi+4wx3bEK40/YXfQmici+xIioKBHQQlJRkk5UysIY00hEQpqaiEgBMDFhUimKG4yJ7EdRFFdEO2axxRjzrTHmDmPM0UmVqDyxYoUt4D7/PN2SKIqihCVaZfEqcALwPLDaGDPPGPOwMaZV8kQrB8yebY8TtVEWF9oNpShJJyplISJ/EZEjsRsc/QuoBfwDWGKMWWGM+acxpksS5VQUJRKqNJUkEpPprIjMEZFhInIi0AZ4HMjDWj7NMsasN8aMNMb0Msa4McstX+jHrShKKcF1gS4iy0TkKRHpDDQH7gNWAbdi9+D+wxjzhjHmgoRIWpbJ5IHZ/v3hmmvSLYWiKGkmIbV/EVknIiNEpCfQFLgZmA1cjp20p5RWpkyBd95JtxThKS0ttAMH4IEHYO/edEuiKDGT8K4iEdkuIq+LSH/smlGXJzoNRQlKJrfQAF56CZ59Fp5+Ot2SKErMJGxbVWPMicB5wD5gvIjsFpE84INEpaEoQSktLYuDB+0xPz858ZeW+6CUSmJuWRhj/maM2WyMaeDjdjbwM/As8DLwkzGmYeLEVBRFUdKJm26o84AVvhsfAf+H3dficeAVoAVwV/ziKUoUaI1aiYb8fNinqxC5xY2yaA4s95wYY5oBnYCXHeuo24FvgYsSImFZRgu5xKD3UYmGc86BmjXTLUWpxY2yqI//XhXdsa2KyT5u8wFdFkRJLZk+wK2klxkz0i1BqcaNstiG/+ZHvbC75832caviMm5FiZ3S0rLwyJkspVZa7kN5ZdUqOOUU2LUr3ZK4wk2BvgC40BjTxhjzJ2Aw8L2I7Pfx0xzYnAD5ygdaI1aUss9TT8GcOfBp6Zx65kZZPAPUBRYCvzj/n/NcNMZUxHZNzUuEgGUarQkmBr2PSmmglL+nMSsLEfkOOB87M/sTYJCIfOnj5TRgo3NNKSvcd593r2tFUcodriblichUYGqIa98BHeMRyoPTSpkHbBSR8xMRZ0ZRmrqf/v1vuO46aNs23ZKUpJTX2BSlNJDQQWhjTH1jTCJt0+7Cx0y3zFHaCrnSpNzKI6XtfVJKFW5mcPc2xjxjjKnv43a4MWYGsB3YaYz5d7yCGWOOBPoDr8cbl1LG0UJSUZKOm26oO4A2IvKgj9uzwOnAb9iNke4yxswSkXjWhRoBPAjUDuXBGDMUGArQuHFjsrOzY06kp3N0EzZemqxYwYnA5i1b+CWF6efl5UWd354+/+fMncu+7SG3Yk8blXJz6QEUivBdQL5iyWuyOeb332kBrF23jtVJkKn6unWc4nOeKflOJm7e5XTdlxO3bKEJsGLFCraEkaGncwwmZ1rfZxGJ6QesBt7yOa+OXTzwK+e8NrAG+DbWuH3iPB87IxzsvZscKUynTp3EFbZe6i5svIwZY9O+/vqUJjt9+vToPXvuD4gsWZI0meJixw4rX40aJS7FlNdk8+STVs5HHklO/CtW+D+vcoCrdzldXHutTf+NN8L7CyNnMt5nYJ5EUS67GbM4HNjkc34KUA14y1E+udjZ3C1dxO2hO3YuxxpgPHCWMebdOOJTFEVR4sCNsjiIbU14OB273MdMH7c9QANcIiIPi8iRItIcGIJtpVzlNj4lQWTqALeOWVj0PihJxI2yWA2c5XM+EFgpIht93I7CDnYr4Ujmx717NxQUJC/+TEILSUVJOm6UxdtAW2PMbGPMd0Bb4P0AP+2ws7vjRkSypSzOsfAlGTX2evXghhsSH28m4+Y+btuW+smGscj53nswfXryZFGUKHGjLF7BjiN0xo4tTAaK94k0xrTBKpDsBMiXOl56yX7EqVrka8kSeNcZhklWzTjRe2eXtW6orVvh8MPhsccSK08iueoqOOusyP4UJcm4We4jX0SuwC5VXldEBojIQR8vW7AzuF9IkIypYdQoe9ywITXptW0LyTKB026Z6Ni61R4//zw16aXrueTkWEU/Zkxy0xkxApYtS24aStpwPYNbRPY4lk+B7ttFZKGI7I5PtHJEomvs5U1ZlLf8hiLUfVi71h5Hjkxu+vfcA506JTeNskCmttAj4GptKABjTA3gEmwroh6wG/gJ+ERE9iZGvBSyZEm6Jch8YnnJ16+3u5I1cG0Up5QmCgvt8cCB9MqhJA1XLQtjTD9gLXaw+x7geuBu53yNMaZsD0hnOplQ0z76aGjRIjVpxZvfVNf0UpXevffarjZPesl6L15/HSq5rncqpQQ3a0OdDHyMbU28B9wAnOcc33PcPzLGaHs0XSSrUIi1kNuzJzlyKNHxn//AzTcnX1m8m8T5soWFsH8/fBDPykFKInDTsngUOwnvdBG5RkTeEpGvnOM1QA/n+iOJFFTJAFJRIx4zBmrV8nZrREMmtKQylUOHkp9Gsu7/+PG2xXLRRTB4MMycGTlMaeWee6BKlXRLERY3yuJ04EMRmRXsoojMBj5y/CnpoDQXnrffDnv3wsGDkf3GSzz36bLLoHv3xMmSCILlJxXvQrA0Cgrif4YffmiPX39tj7vD2Mzk5LibL2NMZpgmjxgB+fnpliIsbpRFXWB9BD/rgDou4i6flBZrqETLmZdn5xHs2OF187QoKsTwaqZjzOLDD+F//4stTDqUuG+aqUz/9NOhWrXExhlK/u3boX59eOIJd/HqpMeocKMsNgFdI/jpDGx2EbdSnnjtNTtD+amnvG4eZeGmYNu7Fy69NPZwpaEldttt7mrqRUVeZbh0aWJl8uB7/zxpzQra8ZAcPPNlPvooOv/33KPjaS5woyymYFeBHeZse1qMMaaCMeY+4GzHX+nj4EG44AKvKe3dd3ubw6WF0lD4hcLTlRBLHnz9RltgpJK1a+0gbTy88gqMGxd7uMD7OG4cnHhiYt+R0rY3+4gR/hUUJSrc2Ls9CVwE/AO42VkfajPQBDu43Rw7i7t0Po05c2DyZLvsx/ffw/PP219pKoATJevmgMZhKk1M3SoLNyQ7X82bw7nnwmmnxZeem3wGFuRXX21bbwUFULmyOznCUVomnJWXRTYTiJvlPrZg14SaBhwDXAU8AFwNtHDce4hI6eyGKi0veyoYMsT/PNEKM1x8nmsisVlGJSr9RPPVV/7nubnw7LPua+X79pV0C5Yf324o8N7LRL7nvukWFSVuGZton0+yn+PTT8PQoclNoxTgalKeiKwRkXOxS5FfiFUUFwJHici5IrI6gTJmDrklVjdxx4oViYknFIn6eFLVrxus4PIUonffbc0no1EspYn774cHHoBJk6IP47lP775rZ8dH8x6JBL+/ibxngXF98UXi4vZl4kQ7d+T6670WUm6JRVkOG2bH10KxdGnwuSYidhytjOB6bSgAEdkoIpNF5D3nuDFyqFJC4Afw0UdQpw6MHg0LF8YX908/xRc+EokqCALjSWWh7Ekr2esZBab52GOw0cVrvH27d6A1GjxmoG7GMj77zB4XL47sN9QzS6aySBZjx9pZ6W+9Zbv1PLhpJbkJc+CATT+w0timje3e8/D223aA/9137Zyh1WWj7hxxzMIY84bLuEVE/uwybPoI9RJ5uhFuvtke4/lAAsPG8uI++aR9EZNVe/MlE5SF73mo+5SoMYt58+zA58yZMGNGbHEcdlhkWRIxD6JmzeBdUKFIxeBzuk21U/VevvqqbdlUrmy7pkJx3XX22KePPa5caZe+KY0tYB+iGeC+zmXcApQ+ZRGKZPXxBjsPx9/+Fnv8sbBypV1a4dFHU/9yT/ExoEuHovIMeiZyQmAwuY1xvwRHLIoCrJHGvHnRyZUoSsO4nxsZPe9HtIPj8XaVZRjRdEO1cPk7Ngnypo54av+RSHZtL5qCYPZsm6dA2/uzz4a//tXuIJesAnvHDujWzbt0tofHH3eXVqDflSvdWbt44klmxcBDItdrWr/exheqe/Paa6OXyw1u4xo71sodSjlHG2+quqHc4vb+5OQkVo44iagsRGSt218qMpBwQn3EySxAPHEXFqbOZn3CBHucOtXf3dOHLlJSFjcv/YEDMHeuv9v771tlFW48Ih5FdcIJdlAyHEVFoa2sUlGQJKKA89wTT4twF1EWAAAgAElEQVTMs4FXNGSCsvA8o+3b3YWfP999+qWh9XPZZemWwI+4BrjLJKFeoliWn4hE4MvtGTCrVAnat098/G7jSEQ8t9wCXbva2q9v3NGkH+480rVISzicey507mz/B1YQ4ilIAo0fIuW1lPdjA4kZM1q50h6jGbT3EItyDJZmrKT6Wf33vxm1P4gqi0A8A9iBJFNZTJjgXR8p2k2YVq2yL/w330SOPxihPhZf91AF9q+/Rv8Se1oV4cxwozHt/OMP//NFi2x/fCgi3YNp00q6eVpSvvLcfXdsg90dOkSW4+9/t62raOQMRzxKrUaNkq1KtySiEJ040R59TVAj5c/zTkVzH2KxVFu2zM6BCUUyWyWBs/S3bUteWjGiyiIcwda8iYcffrBmmcE+rsDCMBKe5Zo9H9emTbZ2FowNG6z8vrVtt7X73Fxo2dLaurslUj8+lOwCO/JI//P27e1idaHii6UAW7DAPz1fOZ5/Hnr29Pe/c2f0cUeSKZ0tC9+F9woL4Zdf4LvvvKa50RKYh3/9y104iO07y8sLHU8ggUv2hEuna1c7BybWLuGHH4a6dUu6x/KMH37Y/9wj56pVaV+VVpVFKAL77ANfri++CF/7CBZfjx5w0knJKSCaNbN99Z60PNSoAUcdZf8HW4o5nDlqMDk91jjffute1miI9x7FGn7v3ujDxLJKaTrNjWPx//e/2zWjzjjD7h+RzHQDMcadIg00YginAAIL/nB+3U6kGz48/omswcYzt26FP/3JtnLTiCqLUERSFuefb2sf0eIZUN6zJ/4alUe+aMIFTvqK9AFG0w0VC8HkDIznzTdDh4sljVBuI0fabqtw+BZYkbocY3lWiRyzSGb3xw8/JC/uUPjm3c14UaCyCHcvA40ZokknHQskBlNqnpZssitoEVBlEYqdOxP7sqxZ4/0fbQGxdWvonc5CfVwLF9pVNUMR2P8eyjKrqCgx+Y+mENi1K3S4SGzdGtzvokVgDDXWroW77opsOHDggFeOSAXJffdFJ1s05OXFNqibSHzvWzxjcolsWRhj78l//hM53li6ZQLf5WgK3nTM9Qn2PWaIEYTush6K337zDkJCYpediPbhN2kCl1ziHfyLhsAB1kDWrYOjjw59fcsWe3zppZJy9usHAwdGL0ushGt9hKJJE9ufG4KG0W5Q1L69HfeJBl/FH4lI+bj9dnssKICKFcP7jTXuSGzfbscpWraMT1m4rVSEamHff7+dLV0pQvEUqCxi6YaaNSt4JSWSfJHSiZdwJvtpNvfVlkU4brghcXHFWhB6Xu6PPw5+3W1BEdhSCfUC/vOfJdNYv97baon2xfUsdrdnT2wzo+PthooVX0URKm/TpsXeJ50htcKg/P67HaeA9BZEgWl75l1EmlgZy/7iwRRaYPhDh9xVWCIRSzzRjK0UFsLrrydvNeYQqLJIFiJw551eU9hwlj7BiPZFiPUjDyyw77uv5L4VblizxpochqJbN+9+Dm4ssVLJpk0l+/DXroVzzoEbb0xOmomYWJYMM9xXXim5vHogbtMNNmYB0c++j6cbKhiBS6mk4x2MRlm8+CLcdJN9NilElUW8FBZ6Tfh8WbcOXnjBdt2AfzM/mpfQ88FE6h4YMya0yWwwPLUpXxkWLLDH33/39xvLx9KiBbRuHd7PTz/Zlz/Uqq6xKlQPif6of/3VWq75xuuROdZdE0MViOH8hSKRyiFS3B5uuw369g0fNtq5QeHS9h2z8FSUInVDxbKkS6TK15dfQoMG/m7JUhbhKngRxklOu+QSr1XU8OHw3HMJFi40qizi5Y47oHZt78tYVGQ3DfLsQex5MWJt3nrii6YvOXA5jXCEa7p/8on/eTg54+m28CincGRC942vDG73Mgk0T43GXzqIdcxiw4bwhhS+jBlj56sE4smzr5m2MV4lEGkMJxZlEany0a9f5AHtZD0j3yXMA9NYt84vn1V8x1k2brTjOylCB7jj5Q1nBXfPAOXWrdZM1mMq6ylUfWcbR2M6G0lZ+MZx5ZXRyxtMWQRTaBD5Axs71soRbKG6eEn1mEUwPv3U+z9S7TpeEtENFQ+xKosBA2xL8cILI/v1dN3ddVfw6zt2+Fui+bYswo1zRdP6/PFHa7QRzK9bs2a3Zu6hKnU9enj/B8rZtat3nlSaB7gzUlkYY44CxgKNAQFGi0iQqkkGEM3qoV984V/wxNINFauFTCTCfXyBBUYkOT1KIhHKIkldLCYe898774zOXxhrrIQqssBVehPF7t2xF0Qe2/9Yxg0WLgxuwhzYfel59yN1Q0XzbD3jZI89FtlvIIl6dh45ly8Pft139YZgefJdVy2NZGo3VAFwn4icBHQD/mKMOSnNMgXHd8VYCP6CnX++/3kwP2PH+p974tu/3/anJopw3VChWjeJJth6VoGEmkEeyq+ba5GItpsjmHluUZEthBORBw+BBV6iukmGDo1toyHfOTiRCmzfPvXADbuCyfvCC14F5NlNMBSetGPp1o2FUPHGWgGJxxoqg8hIZSEim0XkJ+d/LrAcaJZeqUIQ7UfjS7CX55//9D/3fbn79Sv54bgtGMK1LAILjGgLS1/Z16yByZNjFqsEoZTFwYMl1x76+eeQ0dQJrM299FLwwj0Y8SjLYcOgXr3oxzp88+rZn+Ltt2NL0+3+B5s3R98NddlltrXrkTfSex+sT33zZps/z+KZvuTkhF8g0pdYvrlgfsPtqw22S/mee7znnsUzn3sutu1wI32rvvc+g5VFRnZD+WKMaQ50BGYHuTYUGArQuHFjsrOzY46/ZzzCAUWFhVQAvp8xg4JataiybRun+Vzff/Ag1QPC/LZyJX8KE+es995DKlfmVB+3H7Kzya9b1ytvqNVxI7BxzRry8vJYv2EDTk8oCxctYle1ajRbuZLjffzm799P5RDxHMzPp6rn5NFHi90LWremUqy7uQVh9qxZ7F+71u/5ZGdnc/zzz9PMt0sPrEFBCBr5KIbs7Gx6eibBRUH+oUMh8+8bZ+Ply2kV4Nb9lVeoDCx78UWiaRLPnDGDomrVAKg/Zw7tgZ0vvkiDMGGWLlvGtuxsmv7yCy3BTrBzQU5ODvW++87PzfMt9Qw8/+gjAA7u309VYO7s2XSJMp3ff/+dna++SrU//qCNK0n9Obh/P3l5ecxdvZouwN69e5kbUAb0dI7bfviBwwIjiLDr5M4rrqCB7ziDz+ZcP3z9NfnOooE9Cc/iRYvYUadOsb/ly5ez1UfOMwsK8FTTCgsKCNXxvHfvXmoGcXdT7rlCRDL2B9QC5gOXRPLbqVMncYW3DuvuV7GiPW7aJPLuuyIff+x/vXnzkmH+/e/wcdavL7J6tb/btm0iGzfGL++tt8r06dNF7rrL6zZ1qr0XI0f6+61ZM3Q8TZvGL0u434oVIvn5/m4iIr17u48z1uddt250cY4dW9KtQoXY0srLE1m+XKSoyD4PEOnTJ3yYCRNsWqNGxXevGzYMni/f+xXqe1mwILnvQbjfEUfYd3nRInveunXo77t168SmvX179GXIxx/7+3vrrdBlULVqoeMJlYc4AeaJRC6PM7ZlYYypDEwE3hORENOYMwBPV8Ull3jNZSMhEv76rl3Bu0BiXcY8FtatKzmg62Zr0kSxebOduxFIPBYh/fvH5j+e/MfanTBjhpVv1Cho3tx9um4I1h0EiZ08mgwC5Qsnb6K7dxK5mKQv6byfEcjIMQtjjAHGAMtF5N/plicqolUUEN0kukmT/M+NScwGTKHMZIcPL+k3XGGZiFnf4ejVK3g3QTzKwrP9aLRE++HGUhiEwjO2MmmStzspUrzJNKWcNcvfEu+EE+DWW0v6S2eFInCzquXL4YMPvNd99x1JxlhA9eowenRkf7EsLe5GzhSNc2Rqy6I7cDWw2BjjmcH1iIjE+LVnAME+6GiWg/YdWAP7Ibz0UmJkAv9WSqiVLdNdywlUmJBaW/NU5t/zwX/xRUmroVAkQkmFInBV1pUrg1dygi0vnyqCWSB+9JEdhP/0U/8tbhNdoO7ZYwe8oxk7jMX01c0zrVjRLrXTqlVkv3GQkcpCRL4H0jsDJVEEq3m5eXE9u8LFi6ew9V1Rd+nSzLTCCJRp6dLMUxbRWla5SSsWc9ZEE+2cjnj2wY6XYO+sMbZidfHF/u6JvkeB25+6ZXaA3U6473Dp0tDXKkcyxYifjFQWZYpg5oyZVjDfe693lmgm4Vku3UObRNjQxEA0z6l798QsLhi4nSZELuC++cY76SzRRNO9km4892fdOq9bhQpwyy0l/f72W2LTfuSRxMTz+eeJiSfSBMYEkJFjFmWKYHb26ezeCVUAxbKhfapwO28g1aRrhu3o0Xb/kjQvA5E2cnKosmOH/6RXY7z705cnVFmUUdLZsigqom6wLUY7dUq9LGWFZI0dhFtGxEMoS6ZywmmDBvk7JMIIJNn4dgEn6t1JQTdUKbizZZB0KgsROgZb0O3HH1MvS1kh8Hk+9FBi4g1cMj4UX3+dmPTKAqWhlfX1196tdBOlLLRlUUZJp7II1qpQ4iPwg3/mmdSmH2o3xfJIaWhZgHfpkEShyqKMEssezokmAUtxKAGk83kq/mRqyyLQ7PjjjxPbfZkCZaHWUOUNz/7GSuKIZmxBSQ2Z2rI44QT/8+HDrTl8sMmwbtAxixQQy8ZBZYFQW5oqSlkgU5VFMBJpzpvofW+CUIrubJLItDkPiqK4J92rDsRCLMucR0KVRQpI5pIJiqKklrfeSrcE0ZPoQe4ko8pCWxaKoqSDrKx0SxATqixUWShlnQib/ChKNKiy0G4opawTbYVowoTkyqGUalRZ+H5ITZumTw5FSRbRKovLLott7wWlXKHKwvMh/elPMG1aemVRlGQQS+s5nZsZKRmNKgvPh/Svf0H9+l73RK3voyhRMGQQfJysvWvKyLaeSnpRZeFpWVSo4D+hp1Gj9MiTYj49ERYfnm4pygc7qsPLXcC36B7fBn5tCBPawMDBMLEVvH4ybKptr89uBl8f5/X/S0OY0DrGhLVlEZG328O6uumWwjKuDayqH9lfqlFl4fmQjPFfV6Z58+RtLJNBXDwE2t0GhQYKKsDSw4L721QbttUIfi2nGqz1+dBW14M9Vb3nyw6zccfCosb+50sPszLGGnbasbCllv2/oY4tsJccDvsq20LaQ5GBl7rAT86w1Y7q1n84dlSHBU384/jfUfDvUyGvSkn/fx4Af+kP84+w54sPh8sHQcs7vH4GDYabLoSLhtg8d7sJzr3ae/3EO2DIpfZZFBkr74pG9v7nVAshaJMm8Pbb4TPjIT8/On9liAOV4LqLoed1qUkv8PsI5IpB0DHI/k2B7KkK85vC+gjvaaJQZeFRFoEti4EDoVrA13fssamTK8Vk9YTHekGbv9jaayDN7oPDH/Se+xbc7W6F5j5bhh97N/S4wf5fXQ9a/wUeOjt6WT46CdrfCh+eZM9XNrByPdrbphuunvxBaxv241Yw4xg45xpoer+9dtS90OghaHsb1HzUFtKeQv0fp8Pt/aHTzV6/R90bXs7Bl9qPOt95bYb3gO5/hvvOhdpBNlLbUd0e91W2SqXdbaHjntvM5tnDygb+1w9/EJ7ubuVtdbu9/11vChLRuHFwxx1w1VWhEzvySLj8cvvfbcuiZUt34cJRs2bi4wyC5/lFqhwkimPvhu43BL/mebdzHWVSFOZ973EDdL4Zjo7wniYKVRahuqGCrV45f35Jt19+Sag43x8NJsvWxiNhsuwvHAuaWD9zj7AfhcmC504t6e+/x9m0Af6I8I3urgp1HoYR3ez5+rpeeW7vZ/8vdmr3253WyL9Ps838Vzpbf1deAg0ftB+DJx8mCw5VtDVlsMY5AFudlsHTPaDS43DnedZv/Ye84e7qa1svgy+1fgcOhp7Xe2UOdZ/+4+Rhmk89QID9lb3haj8M3QJ2Tl3UGL5xwnj8zmnm78c3XyYLvj/Gup95vVUqsXDCnfB7QNfEIwEKeGVD+FuvgIBDhtilICpUgFODPHiwK6B6NuRxO2bhaxzSo4e7OALp0CHmICYL7usTW5g6jmLPrwgPnAOTT7DxBKuxe57l51HqxrOu9YY51mcbmSWNvdc73AKnXw/tb7Hfg29aFR+Hqy+xlSRPPPOOsGNciwNa38lGlYVHWRgTeRGyevVKugWuJhmKU0+F668H342HfEwat9ewBejpTo2j9V9geSPod6X3RRvbPnjU110EU463/2/vBzUfgYPOUjGnOwXmwMHwrNOrdv+53v8eZh/pLcxedQr0F7rarhlfqj8K9/eBfVXgnr5w9jX+11/q6v3f7lY41aeQvb8P3ObsgPl+O9hZA8643j98XhWoGlC5rRRg+fniKfaYU93rNrIb/OlOYuZvZ9m8zmzudauQFSBTVXt/Jrayfr843rZePLzTzgmX5Ck7xwXZsyqQJ88Mc9HXhPadd+BMx3PduoyYNYIPln7gbVm88AJ8/z2MGYNgFfS8I0JHvWDXMtrcBg+eAzz1FADvtiv5/oRiXBsYeYq/26dNcnimu/d8RDf/Csmzp3lbn570wFZMQjGhNRx/B1R5zL6P5wY0uJ7tDhdcYf8ffa9Nb04z+z1de5HX38s++bpioPVX/VF4prv9P2CIvTa9hdff6vre1iXY1vb0FrCwif32FjWBwiBF0HvtbCXJw6l/tmNcqcZIGZmU1rlzZ5k3b17sAfv0gf/+F776Crp1g7p1bU2soMBu/P7qq16/In4tjs9awinzNtOkdsn5GZNPgA5b4Mg9jsO0adC7t+0TrmL7Pv727WN0ueVJLvjV1oxHdossbr9f4Z5ZYATOvtb/mmR5a9BfvAcN99k+71AUPmFrLpEY9h0MPz2yv0Rw3//sYON2p3XzeDbUOgQPxFhbTDXfvgVnXZduKSyS5Xvi83137Qpz59r/H3xgWwCffAK33YZ5wr7XsuAi+PRTPn9zGJ0H3sERtY9gT7eO1D1vAbUOQu7/BU/z8OEN2XbAbvH6WquHuHHw037vYq1DcMZaeO/UWvxaNY/jd8JVi+y4zZ6q0MNpaf11BlQvgAd/gMrOxPPcf9rWrq+ynDMaug61/y9aDu99bLsWPdwyF175wo61/fUsePq/sKaeN0ysXPczvNXR3+31z+DPP4dutb7xKdxwkb9b/1/hizD1y/N/gckuevS23r+Vw2u6s1QxxswXkc6R/Ol+Fk5ta92hbRwFGPCuDT9iBHTvDtfY6vOWvC00qGibittrwEWXQ+uxZ7PEN76xY+Gaa4prJ+M/hO7roUL+Tgp2r+PouravZ1FjeHLmk3AFzHgTfm5eFTgYUdwpJ9hfMHwHp5ceBg9GKGCHR9lbkCpFAfBcQK3wiZ6pSzseMkVRhMVXcZx9NtSvz7orz+doXz8FBRRUgAFrh9Nq7Gcs+8syCj/5GEYfS15VOxhcrQA21obDb7idys+/CFCsKABuWv40vr12/Z1dAN7+BK49N6/YvXq+HdD35SmnsePbDft0d9sF6Ytvof9pK9uK9WVUF2je62KG5X0C2FbhnlAGAFEQqCgAbhwAVcP02gUqCrBjVeFwoygAcg7kuFYW0aLdUCLMOwKOmX8VoxeOsW5OzZ9q1eDqq6FHDwRo+lxTrhrVhwuf6Vg8+PnLDp8xi+uug8GD/QZ/h1xqB0qbzb6MY0Ycw8RlEwH/Ad8zr4fvmkRWFJHwHRCNpCjADhgrZZT334cxY/zdPMpi7lyoX58Pl37IMSOO4Zvfv/H6KSwk13n9V+esBiC/jncQq+PNkDfoQo68D+6oORPw71rxUBRkyO/ai/3PAxWFL++18/5/6kw7nhCOVQ1KunkUBcSnKMJx9SWx+d8V5F4lgmqVkpRBH1RZFBWx3BlQ/e6PedC3b/GexndPvZvT3zwdpk6l76u2ev3hxq/5756fvcGlqHjgqd/ZW1m3f4tf/2Iggz4chMmCqccnJzuKAsBppzH0sFmYJwwLtyxkzE9jqNv3Z1uRqVCBs94+i8s+shYEZ7/jrbkc3WE6DYbZ/xWNLaFfnvty8fUVh0Gbdt8D8GqVRZgsa2EWSDTdm0riqFoxjC1ugtBuqKKi4lpQxYqV4Msviy89P/t5AO7+7lG+3vxd8ODiHTT88rcvOWbEMcmTVUk7Fy+HT5I10zqAo0w91kuOn1vbrV4rmJFT4M5+JcNtqAMXft6fn3cuBaDDq45VUWU7PlChII/pa6YHTXN9Ve8eC3vz99L33b58teorPz9ri3a6zJHiS8Wi4APabtCWRSoQKVYWYxeOZf6m+RwsOMir87wD2x6lUZZ5OLguLHO02wJ3zI7s75wQ22p/PDe6uTb/mVrSrYqpVCyDL885ZXGrbV63iRNgzBUlV4F981N77LgZ7pgDr0yGTpugaa7Xz0VDKFYUgdzfB7r+cH3Qa8EIVBTlkQo+RmTVXc5ZrBJk+orv846WI/YEd1dlkQqKijjo077q/FpnBn4wkFu+iGIKZRmiaS4s7PVB0GttC4LM0iP4BxArNQ9F5+/wPOi1Gi5cEX3cQxaXdPtkAoz0Nh75+7f2+NgMf3/jPvI/7/07NM6vCnXqcNRu67b0JX8/kgXtt8DxO+DuWfa84Anv9YNDliJZsHCUv8XSvT/a82Uv2efQ5ze4ZDkU4m8z3GgvtHAaGrc5Rk23zIN5o2HTc3CBM3w2P4yJ6xsnw697fg/toZRTpQAOPpnYOHcPtxaBNQ9BbWdosfUfof2fsQZmvuHvtu4/1vrQl3OcxzC4SeQZq57vJPst+674WbwBVSoGWTIgwaiyKCri1vP9nb5Y+UXKkj/psJMie3JB7wjlwYk1myNZMGqSPT9uF7SreSz7Hy25L7Bp5j/b7LIl9oPc+0+4b27lYvcuG71+rvsZagWM2Qe+5KM/t90qAD8c+XhxAf3+R9YcEuDj8TbM1mfh27fhs/Eg229n/IfeeP51jLW9mfGmf3pvfuZN88q9doGlumv8q/WPzbTX/x7QK1MlwMpl2ljYsvAcqFCB7uus25FBankLRsGvL3jPKzpjyqevpcQaTSfuKDkKvOk5+Opd+79pLWuS/dgMK+O2f0GD/SCHHuHGn3wCObOz95eyTuV/9fxn8f+r93gnJHjeyUAuqNwGuXhhyPgkCw4+VfLZ+XJUnaNoklvSvf0WG/6dj0teq1QE//wGdj7tfZ5fv2OP9fbDqjttM7TPb9atyyZrMQa2BShZ0HivnYfjadWueh4GOBWf9ied5ZfeT6NgsJ+JpTUlliw4PkgPoGSBCTaJOMGUe2Xx3wa7Eh7nPd3uoV41O4HP020QigbVg5hxONx0sv8kicW3eqvKz38JR++3g1rj2mSx7EWY9D4sumURC7dfyuT3YcEr8MMNPzDjOltt7v+r90Os7tREhs63Nuv9VgKFhVSuUJlAGlb3b1nsrWI/yEpFMPyo65l30zzm3jSXz3uNZnOLl5j0PoyaDKuftx9FIGveP5zJl0/mxp/gKE+BawyDl8DsX89kyBLHhBmQYN/ACy9w6L2xAFy5CO699lXmjrZ2/B6WvOT9YAFev/pDFt6ykIZ1GluT6AjUPgRzq93JNzOas+xFjzACFSow5nNYtLwXdXyU4e9holz2Ikx5jxL7SvzY6z1WvBA8DED7Ju2Z96qdaxIWJz/JmNH72gWvMX/ofFbduYrlf1keVZgbOpRcy+KDQSVbrXVqNWTlHSuZeuVUTrrYawtb9yBsehbW3u19oItehgkPzIF2XjOpH3yMvdb0+swv7hUbLuKHG36g+1HeWX1Nc2HBLQtoVLukiWk9Z6jmykXw0aUfsflZ77VKRfZ9rFJolTVA5UL7bv/6Ahxb/1h+HgWTxtmC/v+mQaN91l8NT7dVXbvMwXNfwc+j4NhdcOZamNvzfR7q8RBTe7/JzLPfY96r0HELvOVTbsx7FR6d6SPsX/4Cc+awaVRN1v27RFaSRimriySePl1i6NfwYcZ1M5ixZgZ/yy65ZeVzfZ7jr2f8lTf6H8G1Cw5yvWNvXaNyDfbl7yv2N/emuTSr3Yx3Fr3DQ9OsSUnWdPjtst5cdtad/O6YLgLMvnE2bQ5vw5QPKlNjbz5nnnY5rzSeDXt+p90Zl9JqexattgON20J+AyiA9luBo06joKiAR09/lGte+Ac7HdO9SlhLF4OtCQFQWEjFChW5/9T7KZRCDnvqP1QthCu/Hse63esYu3AsL859kb0++qTShRfR6YhO9uRyO6/n/GtuA2Oous/70fhyTG5FjjmhPwDDp9lum843HocBuu61StbWlCTkujhD2gxh/Yv/4J4+t1PBVKCzk4eJE+Do3pfQept/FbFa5eq0a3yiPbnrLt6/eQJtPv3Rz8/ECTDzGLjZmdvZueJRMOBumO5sCORMyqyRD233WnPSJfk38fOk14q7h4LRansQxyuvpF6fC6m3I8g1HzptDuIYOJG2alWoVYs9VfOCePZyS6dbGDV/VPgEA7iq3VVR94c/0fMJDIYHuj/AZVsb0XfzMwC8ev6rnNXC1p7rVavHiHNH8MXKL7i+w/VUrliZPzX4Ewu2LCiO59KlTg2+rncGSNtRH0Nl+/J+OvhTGt18D6etX80jJz7C4J6DOaaxj60t0PJgbTjqNCZdPonR80ez92/DGLwEGjzbgCl3z2Pyr5Op+8a7tPnkf2Q3h8ucIR6zeDEDT2oDF1wOjAPsQLSHKe/BF/eez2H7JnOYz7vdwWmwdnSOzXPghSne1gPz5sFTT1H57beL/QJ0rt8aTAXO7XGdddhsJ6VUK7DKcGf1IO9A5crQpQtNN+cFX5YoWYhImfh16tRJ3EAWMf9avdiqOPxlH14mT3//tHy6/NPi68Vs2SKyeLEM+mCQvPnzm3LNJ9cU+/l+7ffF3oqKirxhQeTrr23w3C3S+qXWsmrnKm+ct91m/eTnS/bqbOkyuoscyD9g3XDSHjrU/9zDK6/IvrFvSL+rV0wAAA4CSURBVMvnWsqPX4zy+vH8Zs4MuDn+ceQdzJNOQ5E5RzjuBQVhbqw33hFjbpLrLjZet6ZNS/iRCRPscdAgkQEDZMmno6Xty21l16DzS8oZLr1nnxXJzS0Z5tdf/f1fdVVJPzVq+J8/84zI8897z/v1E+na1f6fOFHkxBNF1q4VOeWUknGBSF6eyPbtIl9+KTJjhsjSpda9VSvPgy8Z5t137bFDh5L3yPN7+GH/8337RI45Rir+Lfx7u+/QvqDuIiKfLP8k6LWioiK/23bGm2cIWciDXz/o56/327397+/778vVFyMjbu0oIiIFhQXS440e8uXKL4M+vo17NkrLF1rK6l2r/Z7zVR9fJS/OfrFkgNxckc2bZfr06UHfObnqquDvR6h3YOBAkSVL/C5/tPQj6flWz5L3/777Sr6PHTsGf1YgsmyZ9fP55yWvLVgQXE4QOeGE4PE9+GD4fMUIME+iKGMztmVhjOkLPA9UBF4XkeGpTP/ebvfy3LnPAdB7bG++Xf0tHwz6gMs+uoxr2nsXRJowyN9ipc9xPrPhGjeGxo35sI3tYK9VpRZjF45l8a2LaXO4d3GXEv2NYmuOjWs1ZsltAZ2XL7wAzzwDlSpxZvMzmXPTnJLCO+G5JGDG0C23UB0Yld2CbhWDzHIKXETu6qv9FnOrWaUm81b2gk1OB3+wOIJw18m3wTsrgWx/+XzxrMslAp9+SmtgETfB1xeX9BuMLl3skvL33WfPFy+Gtm3t/4cesjsh+tIgSPffggUl1/ryXS9MxFuTa9oUljvdMrNmBa/h1axpf3372vPtThNjsDMbLViYK6+0v3AE3r+KFcGYsGaYpx11GtUre2eEXdH2Ct5f/H7x+3pCQ/98X93uat5Z9E6Jd9PTpQnw7ZpvmbfJNsOGtBnin2CrVoz9BBhll/GtWKEi310f2uTuiNpHsOL2kq38dy5+J3iAWrXsb4VPmK1bYeZMuPRSOPnkkGkVU8mn+Hv4YWjtv1HIwJMGMvCkgfDA4bDNMV3q3x8uuADefBN2+gwgtGgBPzvzr2bMgJwcGDDAvmetHFvr446jBIFb3nrepQYNbNfVypV2FQlfhvjc63Hjgq9Zlwyi0Sip/mEVxCrgWKAKsBA4KVwYty2LYx6sImQhi6dP8Ksprd61uthPr7d6CVnIN79/IwfyD5SobXk4VHBICgrD1LZFZH/+/qDuZCE1n6hqawnr1sWekdNPF7nxRvt/9GgbT3Z2UK/Tp08XWbTI+rnpJm/tZOXKyOn41trD0b+/9XPWWSJ79ojs3SvyzjvWrUkT68cTzy+/iOTkiLRvL7J4sX88AwZYP2PGxF6LCuc/L8+2HAJriO3be8+fecbK7Tm/5RaRU0+1/7//3j++YDXAYOzZI1JYGDpcII89VtLPf/7jf15QIDJunJw81L67H59oj09kPyE5+3Mk92Bu8Xvneb+fnPGkHCo4JIVFXll27tsphUWFcrDgYPExHAWFBXKo4JDsz98f/JvYtMm2nmJlyZLi1nUk/FoWHhYtKpnuzTeLnHaav9sff3jv4Zw5oRNZs8a+uwMG+H8jvs/s6qvt/88+815ftsym4cuWLfbnCfvTTxHzKJMnF/sPmt84IcqWRdoVQ1Ch4FTgK5/zh4GHw4Vxqyz+ePJhmdcUkc2bZeKyicUf0287fiv20++9fkIWkr06eOGbCBZsXiCb9mxKTGRFRSIrVoS8XPzCff21yP791v+GDdHHX6dObIW2h/37bbhnn7XnCxfabp5wLF8u0qOHVVL16iVOWXh48kmRrCyRJ56w5wcP2jSPPtp7T3r1kuLunt9+E/nzn0Xy8/3j8SiRtWtjU2o7d3r9f/55cD+5ubYy0KOH7So7eFDkT38SqV7dhnMKxh3VkdnNkKLjjpWpK6f6KQIP63evl+f+91zESk1pIe7Cc/p0+6zz8mIPe/75Io8/bv/v3Gn/h+ua9WXePNulXFjyGQXFmLQri4xcddYYMwjoKyI3OudXA6eIyO0B/oYCQwEaN27cafz48bEnVlTEgT/+oFoTu+XZlgNb+GrLV1xzzDXFTfCdh3byycZPuL759VQwpd+ALC8vj1q1arkOX2XHDqpu20buiScmUKooKCqyxWqU3V8NZs8mt0IF8rtEuU52HFQ4eJCKe/eS36ABdRctoqBGDfYGdn2F4IjPPmPHKadwsEmTyJ7DUGXHDg7u2EGlZs0oTNHGQekm3ne5tFBl2zaq7N7NliZNEp7fXr16RbXqbNpbEcF+wCDsOIXn/GrgxXBh3LYsRBJQOylllKf8lqe8imh+yzrpbFlkajV5I+C7mO+RjpuiKIqSBjJVWcwFjjfGtDDGVAGGAJ+nWSZFUZRyS0aazopIgTHmduArrGXUGyISfGU0RVEUJelkpLIAEJEpwJR0y6EoiqJkbjeUoiiKkkGoslAURVEiospCURRFiYgqC0VRFCUiGTmD2w3GmG3A2ogeg9MICLaQdFmlPOW3POUVNL9lnWTk9xgROSySpzKjLOLBGDNPopnuXkYoT/ktT3kFzW9ZJ5351W4oRVEUJSKqLBRFUZSIqLKwjE63ACmmPOW3POUVNL9lnbTlV8csFEVRlIhoy0JRFEWJiCoLRVEUJSLlWlkYY/oaY34xxvxmjBmWbnlixRizxhiz2BizwBgzz3FrYIz5rzFmpXOs77gbY8xIJ6+LjDEn+8RzreN/pTHmWh/3Tk78vzlhTYrz94Yx5g9jzBIft6TnL1QaachrljFmo/N8Fxhj+vlce9iR+xdjzLk+7kHfaWe5/9mO+wRn6X+MMVWd89+c682TnVcn3aOMMdONMcuMMUuNMXc57mX1+YbKb+l5xtHskFQWf9ilz1cBxwJVgIXASemWK8Y8rAEaBbg9Awxz/g8Dnnb+9wO+BAzQDZjtuDcAfneO9Z3/9Z1rcxy/xgl7XorzdwZwMrAklfkLlUYa8poF3B/E70nO+1oVaOG8xxXDvdPAB8AQ5/8o4Fbn/23AKOf/EGBCip5tU+Bk539t4FcnX2X1+YbKb6l5xin78DPtB5wKfOVz/jDwcLrlijEPayipLH4Bmjr/mwK/OP9fBS4P9AdcDrzq4/6q49YUWOHj7ucvhXlsjn8BmvT8hUojDXkNVZD4vavYfV9ODfVOO4XldqCS417szxPW+V/J8WfS8Jw/A84py883RH5LzTMuz91QzYD1PucbHLfShABfG2PmG2OGOm6NRWSz838L0Nj5Hyq/4dw3BHFPN6nIX6g00sHtTrfLGz7dJbHmtSGQIyIFAe5+cTnXdzv+U4bTLdIRmE05eL4B+YVS8ozLs7IoC/QQkZOB84C/GGPO8L0otipRZm2jU5G/NN/DV4DjgA7AZuC5NMmRNIwxtYCJwN0issf3Wll8vkHyW2qecXlWFhuBo3zOj3TcSg0istE5/gF8AnQFthpjmgI4xz8c76HyG879yCDu6SYV+QuVRkoRka0iUigiRcBr2OcLsed1B1DPGFMpwN0vLud6Xcd/0jHGVMYWnO+JyMeOc5l9vsHyW5qecXlWFnOB4x0LgirYgZ/P0yxT1Bhjahpjanv+A32AJdg8eCxCrsX2jeK4X+NYlXQDdjtN8a+APsaY+k4TuA+2r3MzsMcY082xIrnGJ650kor8hUojpXgKNIeLsc8XrHxDHCuXFsDx2MHcoO+0U3ueDgxywgfeN09eBwHfOv6TinPPxwDLReTfPpfK5PMNld9S9YxTPbCTST+shcWvWOuCR9MtT4yyH4u1hFgILPXIj+2L/AZYCUwDGjjuBnjJyetioLNPXDcAvzm/633cOzsv7yrgRVI88AmMwzbN87F9sH9ORf5CpZGGvL7j5GUR9oNv6uP/UUfuX/CxUgv1TjvvyxznHnwIVHXcqznnvznXj03Rs+2B7f5ZBCxwfv3K8PMNld9S84x1uQ9FURQlIuW5G0pRFEWJElUWiqIoSkRUWSiKoigRUWWhKIqiRESVhaIoihIRVRaKEgXGmObGGDHGvJVuWRQlHaiyUBSXGGPechRI83TLoijJplJkL4qiYJdMaIVdhE1Ryh2qLBQlCkQkH1iRbjkUJV1oN5SiREHgmIUxRvCut7PauSbGmDUB4RoYY/7PGLPcGLPfGLPbGPONMaZPkDSuc+K4ztkNLdvxr8ssKGlHWxaK4o4ngIuA9sDzQI7j7jlijDkGyMZuavQdMBWoCZwPTDXG3CwirwWJexDQF7u72yjgmKTkQFFiQJWForhARLKcge32wAgRWRPE29vYgv5yERnvcTTG1OP/27tjljiiKAzD70GwCqQyldgHjBZWKcTKxlK72AVSJgohtfkNZjvxJ2yhlRZaq9gF0gdSbFItWMtJMbMQZJLLzu5stngfGC7M4cLpPubeOzNViBxHxHlm/nwybwfYycyLDlqXWnEZSupARKwDW0D/z6AAyMwhcET1NdC9hulnBoXmjU8WUjde1+PziPjcUF+qx5cNtbtOOpImYFhI3Rj943i7vv7mWcO9wfTbkSZjWEjdGL2PcZCZx2PO9fST5o57FlJ7j/W40FC7qcfNGfUidcqwkNob/fR+5WkhM++pjsvuRsTbpskR8SoiXnTYnzQ1LkNJ7V0Bn4CTiOgDD8AwM3t1/Q1wDZxGxAfgluo9jGVgDVil2gj/NevGpXEZFlJLmXkZER+Bd8AhsAh8B3p1/UdEbADvqY7I7lMtWQ2Ab8AX4Ot/aF0aW2S6lyZJ+jf3LCRJRYaFJKnIsJAkFRkWkqQiw0KSVGRYSJKKDAtJUpFhIUkqMiwkSUW/AWn5gIGPO4GPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: [1.1186802]\n"
     ]
    }
   ],
   "source": [
    " \n",
    "\n",
    "'''\n",
    "模型训练\n",
    "'''\n",
    "with fluid.dygraph.guard(place = fluid.CUDAPlace(0)):\n",
    "#with fluid.dygraph.guard():\n",
    "    print(train_parameters['class_dim'])\n",
    "    print(train_parameters['label_dict'])\n",
    "\n",
    "    cnn = MyCNN()\n",
    "    optimizer=fluid.optimizer.AdamOptimizer(learning_rate=train_parameters['learning_strategy']['lr'],\n",
    "                                                parameter_list=cnn.parameters()) \n",
    "    for epoch_num in range(train_parameters['num_epochs']):\n",
    "        for batch_id, data in enumerate(train_reader()):\n",
    "            dy_x_data = np.array([x[0] for x in data]).astype('float32')           \n",
    "            y_data = np.array([x[1] for x in data]).astype('int64')      \n",
    "            y_data = y_data[:, np.newaxis]\n",
    "\n",
    "            #将Numpy转换为DyGraph接收的输入\n",
    "            img = fluid.dygraph.to_variable(dy_x_data)\n",
    "            label = fluid.dygraph.to_variable(y_data)\n",
    "\n",
    "            out = cnn(img)\n",
    "            acc=fluid.layers.accuracy(out,label)#计算精度\n",
    "            loss = fluid.layers.cross_entropy(out, label)\n",
    "            avg_loss = fluid.layers.mean(loss)\n",
    "\n",
    "            #使用backward()方法可以执行反向网络\n",
    "            avg_loss.backward()\n",
    "            optimizer.minimize(avg_loss)\n",
    "             \n",
    "            #将参数梯度清零以保证下一轮训练的正确性\n",
    "            cnn.clear_gradients()\n",
    "            \n",
    "            all_train_iter=all_train_iter+train_parameters['train_batch_size']\n",
    "            all_train_iters.append(all_train_iter)\n",
    "            all_train_costs.append(loss.numpy()[0])\n",
    "            all_train_accs.append(acc.numpy()[0])\n",
    "                \n",
    "            if batch_id % 1 == 0:\n",
    "                print(\"Loss at epoch {} step {}: {}, acc: {}\".format(epoch_num, batch_id, avg_loss.numpy(), acc.numpy()))\n",
    "    draw_train_process(\"training\",all_train_iters,all_train_costs,all_train_accs,\"trainning loss\",\"trainning acc\")  \n",
    "    draw_process(\"trainning loss\",\"red\",all_train_iters,all_train_costs,\"trainning loss\")\n",
    "    draw_process(\"trainning acc\",\"green\",all_train_iters,all_train_accs,\"trainning acc\")\n",
    "    #保存模型参数\n",
    "    fluid.save_dygraph(cnn.state_dict(), \"cnn\")   \n",
    "    print(\"Final loss: {}\".format(avg_loss.numpy()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49358973\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "模型校验\n",
    "'''\n",
    "with fluid.dygraph.guard():\n",
    "    model, _ = fluid.load_dygraph(\"cnn\")\n",
    "    cnn = MyCNN()\n",
    "    cnn.load_dict(model)\n",
    "    cnn.eval()\n",
    "    accs = []\n",
    "    for batch_id, data in enumerate(eval_reader()):\n",
    "        dy_x_data = np.array([x[0] for x in data]).astype('float32')\n",
    "        y_data = np.array([x[1] for x in data]).astype('int')\n",
    "        y_data = y_data[:, np.newaxis]\n",
    "        \n",
    "        img = fluid.dygraph.to_variable(dy_x_data)\n",
    "        label = fluid.dygraph.to_variable(y_data)\n",
    "\n",
    "        out = cnn(img)\n",
    "        acc=fluid.layers.accuracy(out,label)#计算精度\n",
    "        lab = np.argsort(out.numpy())\n",
    "        accs.append(acc.numpy()[0])\n",
    "print(np.mean(accs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **五、模型预测**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第1个样本,被预测为：apple_pie\n",
      "结束\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "\n",
    "def unzip_infer_data(src_path,target_path):\n",
    "    '''\n",
    "    解压预测数据集\n",
    "    '''\n",
    "    if(not os.path.isdir(target_path + \"foods\")):     \n",
    "        z = zipfile.ZipFile(src_path, 'r')\n",
    "        z.extractall(path=target_path)\n",
    "        z.close()\n",
    "\n",
    "\n",
    "def load_image(img_path):\n",
    "    '''\n",
    "    预测图片预处理\n",
    "    '''\n",
    "    img = Image.open(img_path) \n",
    "    if img.mode != 'RGB': \n",
    "        img = img.convert('RGB') \n",
    "    img = img.resize((64, 64), Image.BILINEAR)\n",
    "    img = np.array(img).astype('float32') \n",
    "    img = img.transpose((2, 0, 1))  # HWC to CHW \n",
    "    img = img/255                   # 像素值归一化 \n",
    "    return img\n",
    "\n",
    "\n",
    "infer_src_path = '/home/aistudio/data/data42610/foods.zip'\n",
    "infer_dst_path = '/home/aistudio/data/'\n",
    "unzip_infer_data(infer_src_path,infer_dst_path)\n",
    "\n",
    "label_dic = train_parameters['label_dict']\n",
    "\n",
    "'''\n",
    "模型预测\n",
    "'''\n",
    "with fluid.dygraph.guard():\n",
    "    model, _ = fluid.dygraph.load_dygraph(\"cnn\")\n",
    "    cnn = MyCNN()\n",
    "    cnn.load_dict(model)\n",
    "    cnn.eval()\n",
    "    \n",
    "    #展示预测图片\n",
    "    infer_path='work/infer_apple_pie.jpg'\n",
    "    img = Image.open(infer_path)\n",
    "    plt.imshow(img)          #根据数组绘制图像\n",
    "    plt.show()               #显示图像\n",
    "\n",
    "    #对预测图片进行预处理\n",
    "    infer_imgs = []\n",
    "    infer_imgs.append(load_image(infer_path))\n",
    "    infer_imgs = np.array(infer_imgs)\n",
    "   \n",
    "    for  i in range(len(infer_imgs)):\n",
    "        data = infer_imgs[i]\n",
    "        dy_x_data = np.array(data).astype('float32')\n",
    "        dy_x_data=dy_x_data[np.newaxis,:, : ,:]\n",
    "        img = fluid.dygraph.to_variable(dy_x_data)\n",
    "        out = cnn(img)\n",
    "        lab = np.argmax(out.numpy())  #argmax():返回最大数的索引\n",
    "        print(\"第{}个样本,被预测为：{}\".format(i+1,label_dic[str(lab)]))\n",
    "        \n",
    "print(\"结束\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 1.8.0 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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